Creating aggregate network flow time series in network anomaly detection systems

ABSTRACT

In an embodiment, a computer implemented method receives flow data for one or more flows that correspond to a device-circuit pair. The method calculates a time difference for each flow that corresponds to a device-circuit pair. Based on the calculated time differences and the received flow data, the method updates a probability distribution model associated with the device-circuit pair. Then, the method determines whether a time bucket is complete or open based on the updated probability distribution model.

RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.15/475,743, filed Mar. 31, 2017, the entire contents of which areincorporated herein by reference.

BACKGROUND Technical Field

This field generally relates to monitoring network activities. Morespecifically, embodiments relate to network anomaly detection.

Background

A communication network may, for example, provide a network connectionthat allows data to be transferred between two geographically remotelocations. A network may include network elements connected by links.The network elements may be any type of managed device on the network,including routers, access servers, switches, bridges, hubs, IPtelephones, IP video cameras, computer hosts, and printers. Networkelements can be physical or logical and can communicate with one anothervia interconnected links.

Network anomalies in a communication network may occur for variousreasons. For example, the number of network flows created by anindividual user may appear usually high on a network device. Such highnumber of network flows could be an indication that the user hasinitiated a denial-of-service (DOS) attack.

SUMMARY

In an embodiment, a computer implemented method receives flow data forone or more flows that correspond to a device-circuit pair. The methodcalculates a time difference for each flow that corresponds to adevice-circuit pair. Based on the calculated time differences and thereceived flow data, the method updates a probability distribution modelassociated with the device-circuit pair. Then, the method determineswhether a time bucket is complete or open based on the updatedprobability distribution model.

System and computer-readable medium embodiments are also disclosed.

Further embodiments and features, as well as the structure and operationof the various embodiments, are described in detail below with referenceto accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are incorporated herein and form a part of thespecification.

FIG. 1 a flowchart illustrating an exemplary method for processingnetwork flow data over a time series associated with a device-circuitpair, according to one embodiment.

FIG. 2 provides an exemplary illustration of a time series and how areceived network flow record could impact flow data update for the timeseries.

FIG. 3A illustrates an exemplary probability distribution model.

FIG. 3B illustrates another exemplary probability distribution model.

FIG. 4 is a diagram illustrating an example environment 400 for creatingaggregate network flow time series for network anomaly detection.

In the drawings, like reference numbers generally indicate identical orsimilar elements. Generally, the left-most digit(s) of a referencenumber identifies the drawing in which the reference number firstappears.

DETAILED DESCRIPTION

Network anomaly detection (AD) systems monitor networks for unusualevents or trends. Some network anomaly detection systems rely on networkflow data (e.g., netflow data) to detect anomalies. These detectionsystems have to take into account that network flow records, such asnetflow records, collected from network devices are controlled bytimeout settings (or their default values) on the network devices. For anetwork anomaly detection system to start processing network flow datafor a specific time period (i.e., a time bucket), the AD system usuallyintroduces a time delay after which the AD system can assume that thetime bucket is complete and that the AD system does not expect furthernetwork flow data for the time bucket.

Choosing a timeout delay value can become a complicated and difficulttask. If the AD system uses a single timeout delay value for the wholemonitored network (e.g., the longest timeout value of all the monitorednetwork devices), the AD system could start lagging in detecting networkanomalies. For example, if a network contains many network devices, butonly a few devices have the longest timeout value, then using the singlelongest timeout value would unnecessarily delay the processing ofnetwork flow data for the rest of network devices of the network.

On the other hand, maintaining different timeout delay values for eachnetwork device of the network has its own drawbacks. Keeping track ofindividual timeout delay values for many devices can be complex and doesnot add much improvement to a detection system's responsiveness.

Additional factors that could complicate uniform creation of networkflow time series: (1) network flow is configured on each device andincludes network flow version, timeout settings (or their defaults); (2)human configuration could lead to configuration errors; (3) differentnetwork device vendors have different network flow implementations; and(4) network flow data patterns can vary.

Accordingly, there is a need for determination of appropriate timeoutdelay values that addresses the above problems so that AD systems canprocess the network flow data more efficiently and more effectively.

To overcome the problems of the conventional systems discussed above,embodiments utilize statistical approaches and treat network flowrecords as probability events. AD systems do not have to analyze everysingle piece of flow data to detect network anomalies because a certainamount of trailing flow data may be statistically insignificant for theAD systems to detect anomalies. Determination of when some trailing flowdata becomes statistically insignificant depends on the flow patterns.Embodiments here may maintain statistical values, such as standarddeviation, mean, variance, and skewness, and use these values todetermine appropriate timeout delay values. In this way, the determinedtimeout delay values can properly address different network flowpatterns.

FIG. 1 is a flowchart illustrating a method 100 for processing networkflow data over a time series associated with a device-circuit pair.Network flow data can be netflow data in some embodiments. Network flowdata can also be any other types of data that captures characteristicsof network flows. A time series is a series of time buckets (i.e., timeperiods) with corresponding network flow data. Method 100 may beperformed by a time series module, such as time series module 406described in FIG. 4.

Method 100 begins at step 102 where the time series module receives flowdata for one or more flows that correspond to a device-circuit pair. Thetime series module may receive a network flow record (e.g., netflowrecord) containing flow data. A network flow record may contain datarelated to one or more network flows. For example, the time seriesmodule may receive a network flow record from one or more flow collectorservers that collect network flow data from network devices. Networkflow data, as the term is used herein, is not limited to data from aparticular brand or type of router. The network flow data may include arecord for each data flow. Each data flow may be one or more packets intime proximity with one another having a common protocol identified viaInternet Protocol (IP) addresses and Transport Control Protocol (TCP) orUser Datagram Protocol (UDP) ports. When a certain amount of time passesafter receipt of a packet having these characteristics, the networkdevice determines that the flow has ended, and if the network devicereceives any additional packets with these characteristics, the networkdevice regards the packets as belonging to a new data flow andrepresents them with a new network flow data record. Each network flowrecord, such as a netflow record, may include, but is not limited to,the data flow's (1) source and destination IP addresses, (2) source portnumber and destination port number, (3) type of layer 3 protocol (e.g.,TCP or UDP), (4) start and end times, (5) size (e.g., number of bytes),and (6) input logical interface (ifIndex). The last field, input logicalinterface, is also called a circuit, which can be used to identify auser (e.g., a subscriber to the network services provided by a serviceprovider). Network flow data collection functionality may be configuredon a per-interface basis on a network device. For instance, for someversions of Cisco routers, the ipflow ingress command can be used toenable netflow on an interface. The ip flow-export destination <address><port> command may be used to configure where the netflow data isexported.

In this way, network flow data summarizes certain characteristics of adata flow. Each flow record is created by identifying packets withsimilar flow characteristics and counting or tracking the packets andbytes per flow. The flow details or caches information is exported to aflow collector server periodically based upon flow timers. Expired flowsmay be grouped together into datagrams, such as “netflow export”datagrams, for export.

From the received network flow record, the time series module mayidentify flow data for one or more flows that correspond to thedevice-circuit pair. As described above, a circuit (i.e., input logicalinterface) can be used to identify a user. Further, when the time seriesmodule receives a network flow record that a network device exports to acollector server, the time series module can identify the network devicethat exports the network flow record. In this way, the time seriesmodule may determine a device-circuit pair that corresponds to a userfor a network device.

At step 104, the time series module calculates a time difference foreach flow of the one or more flows that correspond to the device-circuitpair. For example, if a network flow record contains flow data for twodevice-circuit pairs, the first pair including 10 flows, and the secondpair including 20 flows, the time series module would calculate 10 timedifferences for the first device-circuit pair, and calculate 20 timedifferences for the second device-circuit pair.

In one embodiment, the time series module may calculate the timedifference for a flow based on the start time of the flow and thecurrent time (e.g., the start time of the flow minus the current time).In another embodiment, the time series module may calculate the timedifference for a flow based on the end time of the flow and the currenttime (e.g., the end time of the flow minus the current time). In yetanother embodiment, the time series module may calculate the timedifference for a flow based on a combination of the time differencesdescribed in the first two embodiments. In some embodiments, the timeseries module may use the file stamp time of a received network flowrecord as the current time.

At step 108, the time series module updates a probability distributionmodel based on the calculated time differences and the received flowdata. In some embodiments, a probability distribution model isassociated with a device-circuit pair. A probability distribution modelmay include flow data that corresponds to the device-circuit pair, andtime differences for flows that correspond to the device-circuit pair.For example, a probability distribution model may have time differencesin one dimension (e.g., X-axis) and flow data in another dimension(e.g., Y-axis). The flow data in the probability distribution model maybe one or more types of data in the network flow records describedabove. In one embodiment, the time series module may use the number offlows as the flow data (e.g., Y-axis) in the probability distributionmodel.

In other embodiments, the time series module may use other types of data(e.g., the number of bytes) in the network flow records as the flow datain the probability distribution model. The probability distributionmodel may also maintain one or more statistical values such as, a meanvalue, a standard deviation value, a variance value, and/or a skewnessvalue. The time series module may calculate the statistical values (themean value, the standard deviation value, the variance value, and/or theskewness value) from the time differences and the flow data of theprobability distribution model.

In some embodiments, to update the probability distribution model, thetime series module incorporates the received flow data from the receivednetwork flow record and the calculated time differences into theprobability distribution model. For illustration purpose, assume thatthe probability distribution model has the number of flows as the flowdata on the Y-axis. Also for illustration purpose, assume the receivednetwork flow record contains data for 100 flows with a calculated timedifference value of 0 second, and 200 flows with a calculated timedifference value of 2 seconds. The time series module would update theprobability distribution model by incrementing the number of flows by100 that corresponds to time difference at 0 second and incrementing thenumber of flows by 200 that corresponds to time difference at 2 seconds.

The incorporation of the new data in the probability distribution modelcould impact the statistical values (e.g., the mean value and thestandard deviation value) for the probability distribution model. Thus,after the incorporation of the received flow data from the receivednetwork flow record and the calculated time differences into theprobability distribution model, the time series module can update thestatistical values. For example, the time series module can update themean value by calculating the mean value based on the time differencesand the flow data included in the updated probability distributionmodel. The time series module can also update the standard deviationvalue by calculating the standard deviation value based the timedifferences and the flow data included in the probability distributionmodel.

As described above, the time series module may calculate a timedifference with respect to the start time of a flow. Thus, in oneembodiment, the time series module may calculate the mean value and thestandard deviation value with respect to the start times of the flows inthe probability distribution model. The time series module may alsocalculate a time difference with respect to the end time of a flow.Thus, in another embodiment, the time series module may calculate themean value and the standard deviation value with respect to the endtimes of the flows in the probability distribution model. In a thirdembodiment, the time series module may calculate the mean value and thestandard deviation value with respect to a combination of the starttimes and the end times of the flows in the probability distributionmodel. In yet another embodiment, the probability distribution model maymaintain the flow data with respect to multiple types of timedifferences (e.g., one with respect to the start times of the flows andanother with respect to the end times of the flows). Accordingly, thetime series module may calculate multiple sets of mean values andstandard deviation values (e.g., one set with respect to the start timesof the flows and another set with respect to the end times of the flows)for a probability distribution model.

At step 108, the time series module determines whether a time bucket, ofthe time series, is complete or open based on the updated probabilitydistribution model. When the time bucket is determined to be complete,the time series module would ignore further flow data that correspondsto the time bucket. For a completed time bucket, even if there isfurther flow data for this time bucket afterwards, the further flow datawould be statistically insignificant to impact the network anomalyanalysis. Thus, in some embodiments, when the time series moduledetermines a time bucket to be complete, the time series module wouldsend the flow data corresponding to the time bucket to a detectionmodule so that the detection module could process and analyze the flowdata, and detect possible network anomalies based on the analysis. Onthe other hand, when a time bucket is determined open (i.e., notcomplete), the time series module would continue to incorporate furthernetwork flow data for the corresponding time bucket.

To determine whether a time bucket is complete, the time series modulemay first calculate a time delay value for the device-circuit pair basedon data from the probability distribution model associated with thedevice-circuit pair. For example, the time series module may calculatethe time delay value based on the standard deviation value describedabove. In another example, the time series module may calculate the timedelay value based on the standard deviation value and the mean valuedescribed above. For instance, the time series module may calculate thetime delay value to be [2×(standard deviation value)−(mean value)]. Invarious embodiments, the time series module may calculate a time delayvalue based on one or more of the statistical values (e.g., mean,standard deviation, variance, and skewness) from the probabilitydistribution model.

As described above, the time differences in the probability distributionmodel can be calculated with respect to the start times of the flows, orthe end times of the flows. Thus, in one embodiment, the time seriesmodule may calculate the time delay value based on the standarddeviation value and the mean value with respect to the start times ofthe flows. In another embodiment, the time series module may calculatethe time delay value based on the standard deviation value and the meanvalue with respect to the end times of the flows. In yet anotherembodiment, the time series module may calculate the time delay valuebased on multiple sets of standard deviation values and the mean values(e.g., one set with respect to the start times of the flows, and anotherset with respect to the end times of the flows).

After the time series module determines the time delay value for thetime series associated with a device-circuit pair, the time seriesmodule may add the time delay value to the end time of a time bucket tocreate an expiry time for the time bucket. If the current time is beyondthe expiry time for the time bucket, the time series module woulddetermine that the time bucket is complete. Otherwise, the time bucketis determined to be open (i.e., not complete).

As described above, in some embodiments, the time series module mayutilize the file stamp time of the received network flow record as thecurrent time. Thus, using a simple example for illustration purpose,assume the file stamp time of the received network flow record is 8:06pm and the calculated time delay value is +5 minutes. For a time bucketstarting at 7:50 pm and ending at 8:00 pm, the time series module wouldadd the time delay value (+5 minutes) to the end time (8:00 pm) tocreate an expiry time (8:05 pm) for the time bucket. Because the filestamp time (8:06 pm) is beyond the expiry time (8:05 pm), the timeseries module would determine that the time bucket is complete (i.e.,the time bucket is closed for update). That means, the time seriesmodule would ignore further flow data corresponding the time bucketstarting at 7:50 pm and ending at 8:00 pm.

On the other hand, for a second time bucket starting at 8:00 pm andending at 8:10 pm, the time series module would also add the time delayvalue (+5 minutes) to the end time (8:10 pm) to create an expiry time(8:15 pm) for the second time bucket. Because the file stamp time (8:06pm) is not beyond the expiry time (8:15 pm) for the second time bucket,the time series module would determine that the second time bucket isopen. That means, the time series module would continue to take furtherflow data corresponding the second time bucket.

FIG. 2 provides an exemplary illustration of a time series associatedwith a device-circuit pair. FIG. 2 also depicts how a received networkflow record could impact flow data update for the time series. In someembodiments, one time series is created for one device-circuit pair.Time series 202 includes a series of time buckets corresponding to adevice-circuit pair. A time bucket is a time period associated withcorresponding flow data. Each time bucket has a start time and an endtime. For example, time bucket 204 covers a time period from t₀ to t₁.Similarly, time buckets 206, 208, 210, and 212 cover time periods fromt_(n) to t_(n+1), from t_(n+1) to t_(n+2), t_(n+2) to t_(n+3), andt_(n+3) to t_(n+4), respectively. Also in FIG. 2, a time bucket markedwith an “X” denotes that the time bucket is complete. For example, timebucket 204 is complete, and this bucket is marked with an “X.”

The time series module may receive network flow records, such as netflowrecords, from one or more collector servers. For example, after the timeseries module receives network flow record 218, the time series moduleidentifies flow data for flows that correspond to the device-circuitpair. For the received flow data, the time series module determines atime period covered by the received flow data. For instance, verticaltime bar 214 represents the first recorded time for the received flowdata. Vertical time bar 216 represents the last recorded time for thereceived flow data. Accordingly, the time period from vertical time bar214 to vertical time bar 216 represents a time period covered by thereceived flow data, contained in network flow record 218, for one ormore flows that correspond to the device-circuit pair.

As FIG. 2 shows, the time period from vertical time bar 214 to verticaltime bar 216 spans across four time buckets: 206, 208, 210, and 212. Thetime series module may determine whether to update each of these fourtime buckets. As described above with respect to Method 100, the timeseries module determines a time delay value (t_(Δ)) from the probabilitydistribution model (not shown in FIG. 2) corresponding to time series.The time series module then determines whether each time bucket iscomplete. For example, for time bucket 206, the time series module wouldadd the time delay value (t_(Δ)) to the end time (t_(n+1)) of timebucket 206 to calculate an expiry time (t_(n+1)+t_(Δ)). In the exampleshown in FIG. 2, if the current time (e.g., the file stamp time of thereceived network flow record 218) is beyond the expiry time(t_(n+1)+t_(Δ)) for time bucket 206, the time series module woulddetermine that time bucket 206 is complete and time bucket 206 is markedwith an “X,” as shown in FIG. 2. In one embodiment, a current time beingbeyond an expiry time means that the current time is later than theexpiry time. In another embodiment, a current time being beyond anexpiry time means that the current time is later than or equal to theexpiry time.

For time bucket 208, the time series module would add the time delayvalue (t_(Δ)) to the end time (t_(n+2)) of time bucket 208 to calculatean expiry time (t_(n+2)+t_(Δ)). In the example shown in FIG. 2, if thecurrent time (e.g., the file stamp time of the received network flowrecord 218) is not beyond the expiry time (t_(n+2)+t_(Δ)), so the timeseries module would determine that time bucket 208 is open (e.g., notcomplete). Similarly, the time series module may determine that timebuckets 210 and 212 are open. Because time bucket 206 is complete andtime buckets 208, 210, and 212 are open, the time series moduleconsiders time buckets 208, 210, and 212 (and time buckets beyond) astime buckets in progress. Accordingly, the time series module wouldupdate time buckets 208, 210, and 212, but not time bucket 206, withcorresponding flow data in the received network flow record 218.

FIGS. 3A and 3B illustrate two exemplary probability distributionmodels. FIGS. 3A and 3B depict the embodiments where each probabilitydistribution model is associated with a device-circuit pair (i.e., oneprobability distribution model per device-circuit pair). FIG. 3A shows aprobability distribution model associated with device-circuit pair #1.FIG. 3B shows a probability distribution model associated withdevice-circuit pair #2. In other embodiments not shown in FIGS. 3A and3B, a probability distribution model may have a different associationlevel. For example, a probability distribution model may be associatedwith a network device (i.e., one probability distribution model pernetwork device) in one embodiment. In another embodiment, a probabilitydistribution model may be associated with a circuit (i.e., oneprobability distribution model per circuit).

In FIGS. 3A and 3B, the X-axis of the probability distribution models isthe calculated time differences. For illustration simplicity purpose,FIGS. 3A and 3B show the time differences in the unit of seconds dividedby 10 (seconds/10). The Y-axis is the flow data. FIGS. 3A and 3B showthe exemplary embodiments where the number of flows is used as the flowdata for the probability distribution models. In other embodiments,other types of flow data associated with the network flow records may beused as the flow data for the probability distribution models.

FIGS. 3A and 3B show the embodiments where a probability distributionmodel maintains flow data with respect to both the start times of theflows and the end times of the flows. The darker vertical bars (barswith patterns inside) represent the numbers of flows with respect to thestart times of the flows minus the file stamp times. The lightervertical bars (bars without patterns inside) represent the numbers offlows with respect to the end times of the flows minus the file stamptimes. For example, vertical bar 310 represents the number of flowswhose time differences with respect to the flow start times (e.g., theflow start times minus the file stamp times of the corresponding networkflow records) equal −1. Vertical bar 312 represents the number of flowswhose calculated time differences with respect to the flow end times(e.g., the end times minus the file stamp times of the correspondingnetwork flow records) equal −1.

For illustration purpose, assume vertical bar 310 represents 295 flows,which is calculated based on many previous network flow records. Whenthe time series module receives a new network flow record and identifiesa flow whose calculated time difference with respect to the flow starttime equals −1, the time series module may increment vertical bar 310 by1 to 296 flows.

After the time series module updates a probability distribution model,the time series module may calculate statistical values (e.g., a meanvalue and a standard deviation value). For example, in FIG. 3A, mean 302and standard deviation 304 represent the calculated mean value (9.23138)and the calculated standard deviation value (9.28959) with respect tothe flow start times, respectively. Mean 306 and standard deviation 308represent the calculated mean value (10.1462) and the calculatedstandard deviation value (8.71006) with respect to the flow end times,respectively. In FIG. 3B, mean 312 and standard deviation 314 representthe calculated mean value (1.41767) and the calculated standarddeviation value (16.389) with respect to the flow start times,respectively. Mean 316 and standard deviation 318 represent thecalculated mean value (10.3327) and the calculated standard deviationvalue (10.3327) with respect to the flow end times, respectively.

FIGS. 3A and 3B show that network flow patterns for each device-circuitpair could differ. Accordingly, the time series module may calculate adifferent time delay value for each device-circuit pair. In oneembodiment, a time delay value is calculated based on [2×(standarddeviation value)_(flow-star-time)−(mean value)_(flow-star-time)]. Fordevice-circuit pair #1 in FIG. 3A, the time delay value would becalculated based on [2×standard deviation 304−mean 302]. The resultingtime delay value would be 9.3478 (2×9.28959−9.23138), which is about 90seconds (because FIG. 3A displays time in the unit of seconds/10). Fordevice-circuit pair #2 in FIG. 3B, the time delay value would becalculated based on [2×standard deviation 314−mean 312]. The resultingtime delay value would be 31.36033 (2×16.389−1.41767), which is about 5minutes (because FIG. 3B displays time in the unit of seconds/10). Inthis way, different timeout delay values can address different networkflow patterns for each device-circuit pair so that the AD systems canprocess network flow data more efficiently and more effectively.

FIG. 4 is a diagram illustrating an example system 400 for creatingaggregate network flow time series for network anomaly detection. System400 includes network devices 402 a-402 f, one or more collector servers404, and anomaly detection (AD) server 410. Network devices 402 a-402 fmay be any hardware devices that mediate data in a computer network.Networking devices 402 a-402 f may be gateways, routers, networkbridges, modems, wireless access points, networking cables, linedrivers, switches, hubs, and repeaters. Networking devices 402 a-402 fmay also include hybrid network devices such as multilayer switches,protocol converters, bridge routers, proxy servers, firewalls, networkaddress translators, multiplexers, network interface controllers,wireless network interface controllers, ISDN terminal adapters, andother related hardware.

After network devices 402 a-402 f are configured to enable network flowdata collection, network devices 402 a-402 f may send network flow data(e.g., netflow data) to one or more collector servers 404. One or morecollector servers 404 may analyze the network flow data and forward thenetwork flow records (e.g., network flow records) to AD server 410 fornetwork anomaly detection. AD server 410 includes two modules, timeseries module 406 and detection module 408. Time series module 406 mayoperate as described above with respect to FIGS. 1, 2, 3A, and 3B. Forexample, time series module 406 may receive flow data that correspond toa device-circuit pair, calculate a time difference for each flow, updatea probability distribution model based on the calculated timedifferences and the received flow data, and determine whether a timebucket is complete based on the updated probability distribution model.In some embodiment, when time series module 406 determines that a timebucket is complete, time series module 406 sends flow data correspondingto the time bucket to detection module 408 to detect possible networkanomalies. For example, based on the flow data corresponding to the timebuckets, detection module 408 may determine that the number of networkflows are too high for a specific time period, which could indicate thata subscriber has initiated a DOS attack.

FIG. 4 illustrates an embodiment where time series module 406 is acomponent of AD server 410. In another embodiment, time series module406 is a component of a separate server located between one or morecollector servers 404 and AD server 410. In yet another embodiment, timeseries module 406 is a component of one of the collector servers 404.

CONCLUSION

Each of the blocks and modules in FIGS. 1 and 4 may be implemented inhardware, software, firmware, or any combination thereof.

Each of the blocks and modules in FIGS. 1 and 4 may be implemented onthe same or different computing devices. Such computing devices caninclude, but are not limited to, a personal computer, a mobile devicesuch as a mobile phone, workstation, embedded system, game console,television, set-top box, or any other computing device. Further, acomputing device can include, but is not limited to, a device having aprocessor and memory, including a nontransitory memory, for executingand storing instructions. The memory may tangibly embody the data andprogram instructions. Software may include one or more applications andan operating system. Hardware can include, but is not limited to, aprocessor, memory, and graphical user interface display. The computingdevice may also have multiple processors and multiple shared or separatememory components. For example, the computing device may be a part of orthe entirety of a clustered computing environment or server farm.

Identifiers, such as “(a),” “(b),” “(i),” “(ii),” etc., are sometimesused for different elements or steps. These identifiers are used forclarity and do not necessarily designate an order for the elements orsteps.

The present invention has been described above with the aid offunctional building blocks illustrating the implementation of specifiedfunctions and relationships thereof. The boundaries of these functionalbuilding blocks have been arbitrarily defined herein for the convenienceof the description. Alternate boundaries can be defined so long as thespecified functions and relationships thereof are appropriatelyperformed.

The foregoing description of the specific embodiments will so fullyreveal the general nature of the invention that others can, by applyingknowledge within the skill of the art, readily modify and/or adapt forvarious applications such specific embodiments, without undueexperimentation, without departing from the general concept of thepresent invention. Therefore, such adaptations and modifications areintended to be within the meaning and range of equivalents of thedisclosed embodiments, based on the teaching and guidance presentedherein. It is to be understood that the phraseology or terminologyherein is for the purpose of description and not of limitation, suchthat the terminology or phraseology of the present specification is tobe interpreted by the skilled artisan in light of the teachings andguidance.

The breadth and scope of the present embodiments should not be limitedby any of the above-described examples, but should be defined only inaccordance with the following claims and their equivalents.

While the invention is described herein with reference to illustrativeembodiments for particular applications, it should be understood thatthe invention is not limited thereto. Those skilled in the art withaccess to the teachings provided herein will recognize additionalmodifications, applications, and embodiments within the scope thereofand additional fields in which the invention would be of significantutility.

What is claimed is:
 1. A computer implemented method for processing anetwork flow with a device-circuit pair, comprising: obtaining, by aserver, a probability distribution model associated with thedevice-circuit pair, the probability distribution model indicating, fordifferent time differences of prior network flows through thedevice-circuit pair, corresponding numbers of occurrences; determining,by the server, a duration of a time bucket according to the probabilitydistribution model associated with the device-circuit pair; determining,by the server, whether to ignore the network flow according to theduration of the time bucket; and detecting, by the server, a networkanomaly associated with the device-circuit pair according to thedetermination of whether to ignore the network flow.
 2. The method ofclaim 1, wherein determining the duration of the time bucket accordingto the probability distribution model associated with the device-circuitpair includes: determining, by the server, a time delay value accordingto the probability distribution model; and extending, by the server, anend time of the time bucket according to the time delay value.
 3. Themethod of claim 2, wherein determining the time delay value according tothe probability distribution model includes: determining, by the server,the time delay value as a function of a standard deviation value and amean value of the probability distribution model.
 4. The method of claim1, wherein determining whether to ignore the network flow according tothe duration of the time bucket includes: determining, by the server, toignore the network flow, in response to the network flow received by theserver after the time bucket.
 5. The method of claim 4, whereindetecting the network anomaly associated with the device-circuit pairaccording to the determination of whether to ignore the network flowincludes: detecting the network anomaly associated with thedevice-circuit pair without the network flow.
 6. The method of claim 1,wherein determining whether to ignore the network flow according to theduration of the time bucket includes: determining, by the server, toincorporate the network flow, in response to the network flow receivedby the server within the time bucket.
 7. The method of claim 6, whereindetecting the network anomaly associated with the device-circuit pairaccording to the determination of whether to ignore the network flowincludes: detecting the network anomaly associated with thedevice-circuit pair with the network flow.
 8. The method of claim 1,wherein each of the time differences is a difference between i) a starttime or an end time of a corresponding prior network flow at one of thedevice-circuit pair and ii) a file stamp time of the corresponding priornetwork flow received by the server.
 9. A system for processing anetwork flow with a device-circuit pair, comprising: one or moreprocessors; and a non-transitory computer readable storing instructionswhen executed by the one or more processors cause the one or moreprocessors to: obtain a probability distribution model associated withthe device-circuit pair, the probability distribution model indicating,for different time differences of prior network flows through thedevice-circuit pair, corresponding numbers of occurrences, determine aduration of a time bucket according to the probability distributionmodel associated with the device-circuit pair, determine whether toignore the network flow according to the duration of the time bucket,and detect a network anomaly associated with the device-circuit pairaccording to the determination of whether to ignore the network flow.10. The system of claim 9, wherein the instructions that cause the oneor more processors to determine the duration of the time bucketaccording to the probability distribution model associated with thedevice-circuit pair include instructions when executed by the one ormore processors cause the one or more processors to: determine a timedelay value according to the probability distribution model; and extendan end time of the time bucket according to the time delay value. 11.The system of claim 10, wherein the instructions that cause the one ormore processors to determine the time delay value according to theprobability distribution model include instructions when executed by theone or more processors cause the one or more processors to: determinethe time delay value as a function of a standard deviation value and amean value of the probability distribution model.
 12. The system ofclaim 9, wherein the instructions that cause the one or more processorsto determine whether to ignore the network flow according to theduration of the time bucket include instructions when executed by theone or more processors cause the one or more processors to: determine toignore the network flow, in response to the network flow received by thesystem after the time bucket.
 13. The system of claim 12, wherein theinstructions that cause the one or more processors to detect the networkanomaly associated with the device-circuit pair according to thedetermination of whether to ignore the network flow include instructionswhen executed by the one or more processors cause the one or moreprocessors to: detect the network anomaly associated with thedevice-circuit pair without the network flow.
 14. The system of claim 9,wherein the instructions that cause the one or more processors todetermine whether to ignore the network flow according to the durationof the time bucket include instructions when executed by the one or moreprocessors cause the one or more processors to: determine to incorporatethe network flow, in response to the network flow received by the systemwithin the time bucket.
 15. The system of claim 14, wherein theinstructions that cause the one or more processors to detect the networkanomaly associated with the device-circuit pair according to thedetermination of whether to ignore the network flow include instructionswhen executed by the one or more processors cause the one or moreprocessors to: detect the network anomaly associated with thedevice-circuit pair with the network flow.
 16. The system of claim 9,wherein each of the time differences is a difference between i) a starttime or an end time of a corresponding prior network flow at one of thedevice-circuit pair and ii) a file stamp time of the corresponding priornetwork flow received by the system.
 17. A non-transitory computerreadable medium for processing a network flow with a device-circuitpair, the non-transitory computer readable medium storing instructionswhen executed by one or more processors cause the one or more processorsto: obtain a probability distribution model associated with thedevice-circuit pair, the probability distribution model indicating, fordifferent time differences of prior network flows through thedevice-circuit pair, corresponding numbers of occurrences; determine aduration of a time bucket according to the probability distributionmodel associated with the device-circuit pair; determine whether toignore the network flow according to the duration of the time bucket;and detect a network anomaly associated with the device-circuit pairaccording to the determination of whether to ignore the network flow.18. The non-transitory computer readable medium of claim 17, wherein theinstructions that cause the one or more processors to determine theduration of the time bucket according to the probability distributionmodel associated with the device-circuit pair include instructions whenexecuted by the one or more processors cause the one or more processorsto: determine a time delay value according to the probabilitydistribution model; and extend an end time of the time bucket accordingto the time delay value.
 19. The non-transitory computer readable mediumof claim 18, wherein the instructions that cause the one or moreprocessors to determine the time delay value according to theprobability distribution model include instructions when executed by theone or more processors cause the one or more processors to: determinethe time delay value as a function of a standard deviation value and amean value of the probability distribution model.
 20. The non-transitorycomputer readable medium of claim 17, wherein the instructions thatcause the one or more processors to determine whether to ignore thenetwork flow according to the duration of the time bucket includeinstructions when executed by the one or more processors cause the oneor more processors to: determine to ignore the network flow, in responseto the network flow received by a server after the time bucket.